Translating and Rewriting Chinese Proverbs: A Case Study of Howard Goldblatt’s English Translation of Mo Yan’s “Shengsi Pilao”
Bibliographic record
Abstract
Howard Goldblatt’s translation of Mo Yan’s novels remains controversial because he has made various changes in his translation. As a result, a lot of original messages in Mo Yan’s novels were not completely conveyed. In this paper, this author compared and analyzed several examples of Chinese proverbs selected from Mo Yan’s novel “Shengsi Pilao” and their translation in “Life and Death Are Wearing Me Out” translated by Howard Goldblatt, in an attempt to investigate how Goldblatt coped with linguistic and cultural challenges in the examples. Findings indicate that based on rewriting, Goldblatt has basically used six translation methods to translate Mo Yan’s Chinese proverbs in the novel into English and his transcreation which was previously neglected can be uncovered in his translation of the proverbs. This study can help other translators reflect on how to translate proverbs in other Chinese literary works into English and provide valuable references to researchers who intend to conduct research into this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".